Impact of international accounting standards on Hungary’s financial transparency

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Acceptance and implementation of international financial reporting standards ensure a wider scope for financial transparency, accountability, and comparability on a global scale. Against this backdrop, this study looks at the implications of these standards on Hungary’s financial transparency by evaluating panel data from 716 private companies over the period 2013–2023. The Hausman test results suggest that Fixed and Random Effects models should be used.
The analysis indicates that, on average, the sampled companies have improved financial transparency by 75%. Key determinants include standard adoption (0.025 coefficient, t = 8.333, p < 0.001), cost of implementation (2.400 coefficient, t = 24.000, p < 0.001), investor confidence (0.035 coefficient, t = 11.667, p < 0.001), and legislative changes (2.450 coefficient, t = 24.500, p < 0.001). Moreover, it is possible to obtain significant positive effects on the centered variables for implementation costs (coefficient = 2.498, p < 0.001) and government efficiency (coefficient = 0.036, p < 0.001).
These results demonstrate a positive effect, which is significantly created by adopting these standards on financial transparency. They underline increased investor confidence and government efficiency as drivers of these improvements. Applying these standards in Hungary’s financial reporting system is classified as a strategic tool to foster economic stability and attract foreign investment, which ensures Hungary’s good standing in the global economy.

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    • Table 1. Sample distribution
    • Table 2. Variable description
    • Table 3. Descriptive analysis table
    • Table 4. Hausman test results
    • Table 5. Variance Inflation Factors (VIF) results
    • Table 6. Stationarity test results (Augmented Dickey-Fuller test)
    • Table 7. Breusch-Pagan test for heteroscedasticity.
    • Table 8. Wald Tests for coefficient significance
    • Table 9. Correlation matrix
    • Table 10. Quartile distribution of variables
    • Table 11. T-tests between quartiles (p-values only)
    • Table 12. Fixed effects regression model (FEM)
    • Table 13. FEM results with centered IC variable
    • Table 14. Fixed Effects Regression Model (FEM) with cantered variables
    • Table 15. Coefficient comparisons
    • Conceptualization
      Abdulhadi Ramadan
    • Funding acquisition
      Abdulhadi Ramadan
    • Investigation
      Abdulhadi Ramadan
    • Project administration
      Abdulhadi Ramadan
    • Resources
      Abdulhadi Ramadan
    • Software
      Abdulhadi Ramadan
    • Supervision
      Abdulhadi Ramadan
    • Validation
      Abdulhadi Ramadan
    • Visualization
      Abdulhadi Ramadan
    • Writing – review & editing
      Abdulhadi Ramadan
    • Data curation
      Amer Morshed
    • Formal Analysis
      Amer Morshed
    • Methodology
      Amer Morshed
    • Writing – original draft
      Amer Morshed